Cluster Agnostic Network Lasso Bandits
Authors: Sofien Dhouib, Steven Bilaj, Behzad Nourani-Koliji, Setareh Maghsudi
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we support our theoretical findings by experimental evaluation against graph bandit multi-task learning and online clustering of bandits algorithms. ... 6 Experiments ... Our experimental evaluations highlight the advantage of our method, especially when either the number of dimensions or nodes increases. |
| Researcher Affiliation | Academia | Sofien Dhouib EMAIL Department of Computer Science, Technical University of Darmstadt Faculty of Science, University of Tübingen Steven Bilaj EMAIL Faculty of Science, University of Tübingen Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum Behzad Nourani-Koliji EMAIL Faculty of Science, University of Tübingen Setareh Maghsudi EMAIL Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum |
| Pseudocode | Yes | Algorithm 1: Network Lasso Policy Input: T, α0 > 0, G, δ Initialization: ˆΘ(0) = 0 R|V| d for t {1, ..., T} do Draw a user m(t) V uniformly at random Observe context set A(t) Select x(t) arg max x A(t) D ˆθm(t 1), x E Receive payoff y(t) Update α(t) via equation 3 Update ˆΘ(t) via equation 4 |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | We support our theoretical findings by extensive numerical experiments on simulated data that prove the advantage of our algorithm over other related approaches. ... The graph used is weightless and generated using a stochastic block model to ensure a cluster structure, where an edge is constructed with probability p within clusters and q between clusters. |
| Dataset Splits | No | The paper uses simulated data for a bandit setting, which involves sequential interactions over a horizon T, rather than static datasets with explicit training/test/validation splits. Therefore, it does not specify such splits. |
| Hardware Specification | Yes | The experiments have been conducted with an intel i7 CPU with 12 2.6 GHz cores and 32 GB of RAM. |
| Software Dependencies | No | We implement the Primal-Dual algorithm proposed in Jung (2020) to solve the Network Lasso problem but we do not vectorize the matrices (in the sense of stacking their columns into a vector), which speeds up computation. The paper mentions implementing an algorithm but does not specify any particular software libraries or their version numbers. |
| Experiment Setup | Yes | We compare our algorithm with α0 = 1 to several baselines of the literature. ... The graph used is weightless and generated using a stochastic block model to ensure a cluster structure, where an edge is constructed with probability p within clusters and q between clusters. ... (a) |V| = 200, |P| = 25, d = 10, p = 0.5, q = 0.05 |